Dynamic fuzzy logic elevator group control system for energy optimization

High-rise buildings with a considerable number of elevators represent a major logistic problem concerning saving space and time due to economic reasons. For this reason, complex Elevator Group Control Systems are developed in order to manage the elevators properly. Furthermore, the subject of energy is acquiring more and more industrial relevance every day as far as sustainable development is concerned. In this paper, the first entirely dynamic Fuzzy Logic Elevator Group Control System to dispatch landing calls so as to minimize energy consumption, especially during interfloor traffic, is proposed. The fuzzy logic design described here constitutes not only an innovative solution that outperforms usual dispatchers but also an easy, cheap, feasible and reliable solution, which is possible to be implemented in real industry controllers

A brief review on vertical transportation research and open issue

Book of Proceedings of the International Joint Conference-CIO-ICIEOM-IIE-AIM (IJC 2016), "XX Congreso de Ingeniería de Organización", "XXII International Conference on Industrial Engineering and Operations Management, "International IISE Conference 2016, "International AIM Conference 2016". Donostia-San Sebastian (Spain), July 13-15, 2016Vertical transportation refers to the movements of people in buildings. High-rise buildings have emerged as a common construction nowadays. In such buildings, the vertical transportation is extremely difficult to manage, specially, when the people arrive at the same time at specific floors wanting to travel to other floors. To solve such situations, the installation of elevator group control systems (EGCS) is a usual practice. EGCS are used to manage multiple elevators in a building to efficiently transport passengers. EGCSs need to meet the demands by assigning an elevator to each landing call while optimizing several criteria. This paper reviews the most relevant contributions in vertical transportation industr

Intelligent control based on fuzzy logic and neural net theory

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment

Development of a Fuzzy Logic Model-Less Aircraft Controller

The Modeling and Control for Agile Aircraft Development (MCAAD) group at NASA Langley Research Center(LaRC) is developing techniques for Real-Time Global Modeling (RTGM) and Robust Learning Control (RLC) for NASA’s Transformational Tools and Technologies Project. This project seeks to develop a systematic approach to reduce the iterative nature of aircraft design by introducing a model-less control law and enabling inflight aerodynamic modeling and controller design. The development of the flight control system without prior knowledge of the aircraft aerodynamic model makes use of TakagiSugeno-Kang fuzzy logic inference systems for pitch and roll controllers and are tested in various simulations and wind tunnel platforms. These fuzzy logic controllers are not based on a mathematical model but rather on a rule base of generic flight control laws generated from the designer’s knowledge of aircraft flight mechanics. The controller architecture uses two channels to provide absolute and incremental controller commands as needed. The absolute channel is designed to reject disturbances and decrease rise time, while the incremental channel provides tracking and reduced steady state error. To provide controllers with acceptable performance without the need for tuning, a general method for selecting input and output scaling gains for the fuzzy inference systems is proposed. A performance and robustness comparison of similarly configured Type-1 and Interval Type-2 fuzzy logic controllers is made. The fuzzy logic controllers were implemented on an aircraft model in the NASA Langley 12-Foot low speed tunnel mounted on a free-to-pitch and free-to-roll rig. The development of the controller architectures and wind tunnel results are presented

A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

High-rise buildings require the installation of complex elevator group control systems (EGCS). In vertical transportation, when a passenger makes a hall call by pressing a landing call button installed at the floor and located near the cars of the elevator group, the EGCS must allocate one of the cars of the group to the hall call. We develop a Particle Swarm Optimization (PSO) algorithm to deal with this car-call allocation problem. The PSO algorithm is compared to other soft computing techniques such as genetic algorithm and tabu search approaches that have been proved as efficient algorithms for this problem. The proposed PSO algorithm was tested in high-rise buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars. Results from trials show that the proposed PSO algorithm results in better average journey times and computational times compared to genetic and tabu search approaches

Fuzzy Optimization Control: From Crisp Optimization

This section shows interesting contents from the development results of author’s past crisp optimization combustion control concerning real boilers of fossil power plants to the upper and lower separation new fuzzy optimization control system plan. The fuzzy decision-type optimization is for elevators and the fuzzy table-like control with zero is for a single-element level control of one tank model. In addition, other researchers’ recent researches concerning other applications are introduced to maintain fairness and balance

Una revisión del estado del arte de los problemas asociados al transporte vertical mediante ascensores en edificios

El transporte vertical es una disciplina que estudia los movimientos de personas en edificios. Los edificios altos se han convertido en una construcción común hoy en día. En dichos edificios, el transporte vertical es un problema que requiere un enfoque sistemático y ordenado. Así, para casos extremos en determinados edificios singulares, la ordenación del transporte vertical se convierte en un problema muy difícil de manejar, especialmente cuando diferentes personas llegan casi al mismo tiempo a plantas específicas deseando viajar hasta otras plantas de destino. Para resolver tales situaciones, la instalación de sistemas de control de grupos de ascensores (conocidos en inglés como Elevator Group Control Systems, EGCS) es una práctica habitual. Los EGCS se utilizan para gestionar ascensores coordinados múltiples en un edificio con el objeto de transportar pasajeros de manera eficiente. Los EGCS deben satisfacer las demandas asignando un ascensor a cada llamada de planta, realizando el despacho de ascensores atendiendo a diferentes criterios de optimización. Este artículo realiza una revisión sistemática y muestra distintas clasificaciones de las contribuciones más relevantes en la industria del transporte vertical, abordando tanto la revisión de la literatura científica, como las patentes en la industria y los trabajos recogidos en revistas de carácter profesional.Plan Nacional de I+D TI-331/2002Plan Nacional de I+D DPI2010- 15352Consejería de Innovación, Ciencia y Empresa de la Junta de Andalucía P07-TEP-0283

Design and Implementation of Embedded Based elevator Control System

The elevator control system is one of the important aspects in electronics control module in automotive application. Here elevator control system is designed with different levels. First the elevator control system is implemented for multi-storage building. This implementation is based on FPGA based Fuzzy logic controller for intelligent control of elevator group system. This proposed approach is based on algorithm which is developed to reduce the amount of computation required by focusing only on relevant rules and ignoring those which are irrelevant to the condition for better performance of the group of elevator system. Here only two inputs are considered i.e. elevator car distance and number of stops. Based on these data the fuzzy controller can calculate the Performance Index (PI) of each elevator car, the car which has maximum PI gives the answer to the hall calls. This would facilitate reducing the Average Waiting Time (AWT) of the passenger. In the second level, the dispatching algorithm is implemented for multi-storage building. Here six types of dispatching algorithms are considered. Based on the traffic situation and condition, one algorithm out of six is operated, that should reduce the Average Waiting Time of passenger and reduce the power consumption of elevator system. The hardware part of the work consists of a simple D. C. Motor, which can control the up and down movement of the elevator car. This D. C. Motor is controlled through the MC9S12DP256B microcontroller. Here four floor elevator systems have been considered and every floor has two switches, one switch is used for up movement and another switch is used for down movement. Based on the switch pressed, the elevator car can move either in upward or downward direction. Here two sensors are used in every floor. One sensor is used for detecting the elevator car when elevator car reached to its destination floor. This sensor detects the car and stops the D.C. Motor. At the same time, another sensor is used for opening and closing the door. Finally, a novel fuzzy based PID controller algorithm is implemented in the MC9S12dp256B microcontroller. This algorithm is mainly used for maintaining the constant speed of D.C. Motor with different load conditions

Model-Less Fuzzy Logic Control for the NASA Modeling and Control for Agile Aircraft Development Program

The NASA Modeling and Control for Agile Aircraft Development (MCAAD) program seeks to develop new ways to control unknown aircraft to make the aircraft development cycle more efficient. More specifically, there is a desire to control an aircraft with an unknown mathematical model using only first principles of flight. In other words, rather than using a rigorously developed mathematical model combined with wind-tunnel tests, a controller is sought which would allow one to bypass the development of a rigorous mathematical model and enter wind-tunnel testing more directly. This paper presents the design of a fuzzy PID controller, governed by a fuzzy supervisory system which incorporates knowledge of first principles of flight, to control a model-less aircraft\u27s pitch dynamics in a free-to-pitch wind-tunnel environment. This hybrid structure is implemented using a PID controller constructed from independent fuzzy inference systems and augmented in real time by a supervisory system also constructed of independent fuzzy inference systems. Experimental results of the pitch control performance and real-time adaptivity capabilities are presented for both aerodynamically stable and unstable aircraft models